Jobs

0

Votes

50

Fans

11

Jobs

0

Votes

0

Hacker News, Reddit, Stack Overflow Stats

GitHub Stats

Description

What is
Flask?

Flask is intended for getting started very quickly and was developed with best intentions in mind.

What is
Trails?

Trails is a modern, community-driven web application framework for node.js. It builds on the pedigree of Rails and Grails to accelerate development by adhering to a straightforward, convention-based, API-driven design philosophy.

What is
Javalin?

Javalin started as a fork of the Spark framework but quickly turned into a ground-up rewrite influenced by express.js. Both of these web frameworks are inspired by the modern micro web framework grandfather: Sinatra, so if you’re coming from Ruby then Javalin shouldn’t feel too unfamiliar.

Want advice about which of these to choose?Ask the StackShare community!

Reviews of Flask, Trails, and Javalin

mjhea0

Small, Powerful, Easy to use

June 28, 2015 08:25

Flask is a light, yet powerful Python web framework perfect for quickly building smaller web applications. It's a "micro-framework" that's easy to learn and simple to use, so it's perfect for those new to web development as well as those looking to rapidly develop a web application.

Ease of Use
Documentation
Reliability
Support

tjwebb

Highly modular and lightweight

January 03, 2017 11:47

The number one thing I like about Trails is that it does not force me to use any particular web server or database layer. I can choose the web server (hapi, express, etc), ORM layer (bookshelf, sequelize, waterline) and everything works with a common configuration.

Ease of Use
Documentation
Reliability
Support

How developers use Flask vs Trails vs Javalin

I use Flask for times when I need to create a REST API that interfaces with other Python code, or there is no specific reason why I'd want to use Node.JS. I prefer Flask because of its small learning curve, allowing me to get started coding as quickly as possible

This lightweight web framework enables quick REST API development while enabling easy clustering, and the usage of multiple worker processes required to scale the REST API service to meet high volume requirements.